lzccccc / 3d Bounding Box Estimation For Autonomous Driving
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3D Bounding Box Estimation for Autonomous Drinving
This project fully implemented paper "3D Bounding Box Estimation Using Deep Learning and Geometry" based on previous work by image-to-3d-bbox(https://github.com/experiencor/image-to-3d-bbox).
Depandency:
- Python 3.6
- Tensorflow 1.12.0
Modifications and Improvements:
-
No prior knowledge of the object location is needed. Instead of reducing configuration numbers to 64, the location of each object is solved analytically based on local orientation and 2D location.
-
Add soft constraints to improve the stability of 3D bounding box at certain locations.
-
MobileNetV2 backend is used to significantly reduce parameter numbers and make the model Fully Convolutional.
-
The orientation loss is changed to the correct form.
-
Bird-eye view visualization is added.
Results on KITTI raw data:
MobilenetV2 with ground truth 2D bounding box.
Video: https://www.youtube.com/watch?v=IIReDnbLQAE
Train and Evaluate:
First prepare your KITTI dataset in the following format:
kitti_dateset/
├── 2011_09_26
│ └── 2011_09_26_drive_0084_sync
│ ├── box_3d <- predicted data
│ ├── calib_02
│ ├── calib_cam_to_cam.txt
│ ├── calib_velo_to_cam.txt
│ ├── image_02
│ ├── label_02
│ └── tracklet_labels.xml
│
└── training
├── box_3d <- predicted data
├── calib
├── image_2
└── label_2
To train:
- Specify parameters in
config.py
. - run
train.py
to train the model:
python3 train.py
To predict:
- Change dir in
read_dir.py
to your prediction folder. - run
prediction.py
to predict 3D bounding boxes. Change-d
to your dataset directory,-a
to specify which type of dataset(train/val split or raw),-w
to specify the training weights.
To visualize 3D bounding box:
- run
visualization3Dbox.py
. Specify-s
to if save figures or view the plot , specify-p
to your output image folders.
Performance:
w/o soft constraint | w/ soft constraint | ||||||||
---|---|---|---|---|---|---|---|---|---|
backbone | parameters / model size | inference time(s/img)(cpu/gpu) | type | Easy | Mode | Hard | Easy | Mode | Hard |
VGG | 40.4 mil. / 323 MB | 2.041 / 0.081 | AP2D | 100 | 100 | 100 | 100 | 100 | 100 |
AOS | 99.98 | 99.82 | 99.57 | 99.98 | 99.82 | 99.57 | |||
APBV | 26.42 | 28.15 | 27.74 | 32.89 | 29.40 | 33.46 | |||
AP3D | 20.53 | 22.17 | 25.71 | 27.04 | 27.62 | 27.06 | |||
mobileNet v2 | 2.2 mil. / 19 MB | 0.410 / 0.113 | AP2D | 100 | 100 | 100 | 100 | 100 | 100 |
AOS | 99.78 | 99.23 | 98.18 | 99.78 | 99.23 | 98.18 | |||
APBV | 11.04 | 8.99 | 10.51 | 11.62 | 8.90 | 10.42 | |||
AP3D | 7.98 | 7.95 | 9.32 | 10.42 | 7.99 | 9.32 |
cpu: core i5 7th
gpu: NVIDIA TITAN X